code: def test_radius_graph_large(dtype, device):
      x = torch.randn(1000, 3, dtype=dtype, device=device)
  
      edge_index = radius_graph(
          x,
          r=0.5,
          flow="target_to_source",
          loop=True,
          max_num_neighbors=2000,
      )
  
      tree = scipy.spatial.cKDTree(x.cpu().numpy())
      col = tree.query_ball_point(x.cpu(), r=0.5)
      truth = set([(i, j) for i, ns in enumerate(col) for j in ns])
  
      assert to_set(edge_index.cpu()) == truth
  
file_path: pytorch_cluster/test/test_radius.py
imports: from itertools import product
  import pytest
  import scipy.spatial
  import torch
  from torch_cluster import radius, radius_graph
  from torch_cluster.testing import devices, grad_dtypes, tensor
label_desc: Use image node_id 4 for calling a global function with example usage: test_radius_graph_large(dtype, device) without return types